Convergence monotonicity and speed comparison of iterative learning control algorithms for nonlinear systems
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发布时间：2025-04-30
发布时间：2025-04-30
论文名称：Convergence monotonicity and speed comparison of iterative learning control algorithms for nonlinear systems
发表刊物：IMA Journal of Mathematical Control and Information
摘要：In this paper, conventional first- and second-order proportional-derivative-type (PD-type) iterative learning
control (ILC) updating algorithms are discussed for a type of nonlinear time-invariant system. On the
basis of the Bellman–Gronwall inequality, the convergence is derived in the sense that the tracking error
is measured in the form of the Lebesgue-p norm. This analysis shows that, under an appropriate condition,
the first-order PD-type ILC updating law is monotonically convergent while the second-order law
is convergent and the monotonicity is guaranteed after finite iterations. Further, by analysing the characteristic
polynomial of the second-order PD-type ILC updating law, an argument about the comparison of
convergence speed in terms of Qp-factor is made. The argument clarifies that the second-order PD-type
ILC law can be Qp faster, equivalent or slower than the first-order law, depending upon different sets of
the learning gains. Numerical simulations are conducted to show their validity and effectiveness.
合写作者：Xiaoe Ruan, Jianyong Zhao
卷号：(2013) 30,
页面范围：473–486
是否译文：否
发表时间：2013-07-01